ArticleScientific reports2016
Rsite2: an efficient computational method to predict the functional sites of noncoding RNAs.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- In silico designing of small molecules for targeting RNA: current landscape and future directions.Journal of computer-aided molecular design · 2026Review
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- Geometric Deep Learning Reveals Ligandable and Cryptic RNA Binding Small Molecule Pockets (SMARTPocket).bioRxiv : the preprint server for biology · 2026Article
- CoBRA: compound binding site prediction using RNA language model.Briefings in bioinformatics · 2026Article
- Computational Ligand-Binding Site Prediction.Advances in experimental medicine and biology · 2026Review
- DistRMI: a deep distance-aware neural network for explainable RNA loop motif-small molecule interaction prediction.Briefings in bioinformatics · 2025Article
- Advances in Methods for Accurate Prediction of RNA-Small Molecule Binding Sites: From Isolated to AI-Integrated Strategies.Pharmaceuticals (Basel, Switzerland) · 2025Review
- RNA language model and graph attention network for RNA and small molecule binding sites prediction.Bioinformatics (Oxford, England) · 2025Article
- Review
- MVRBind: multi-view learning for RNA-small molecule binding site prediction.Briefings in bioinformatics · 2025Article
- Advances and Mechanisms of RNA-Ligand Interaction Predictions.Life (Basel, Switzerland) · 2025Review
- AI-integrated network for RNA complex structure and dynamic prediction.Biophysics reviews · 2024Review
- Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN.Nature communications · 2024Article
- Advances in machine-learning approaches to RNA-targeted drug design.Artificial intelligence chemistry · 2024Article
- RNet: a network strategy to predict RNA binding preferences.Briefings in bioinformatics · 2023Article
- Understanding the promising role of antibody drug conjugates in breast and ovarian cancer.Heliyon · 2023Article
- Dissecting and predicting different types of binding sites in nucleic acids based on structural information.Briefings in bioinformatics · 2022Article
- Structure-based deep learning for binding site detection in nucleic acid macromolecules.NAR genomics and bioinformatics · 2021Article
- RBinds: A user-friendly server for RNA binding site prediction.Computational and structural biotechnology journal · 2020Article
- RNA-ligand molecular docking: advances and challenges.Wiley interdisciplinary reviews. Computational molecular scienceArticle
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2 authors.
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Abstract
Noncoding RNAs (ncRNAs) represent a big class of important RNA molecules. Given the large number of ncRNAs, identifying their functional sites is becoming one of the most important topics in the post-genomic era, but available computational methods are limited. For the above purpose, we previously presented a tertiary structure based method, Rsite, which first calculates the distance metrics defined in Methods with the tertiary structure of an ncRNA and then identifies the nucleotides located within the extreme points in the distance curve as the functional sites of the given ncRNA. However, the application of Rsite is largely limited because of limited RNA tertiary structures. Here we present a secondary structure based computational method, Rsite2, based on the observation that the secondary structure based nucleotide distance is strongly positively correlated with that derived from tertiary structure. This makes it reasonable to replace tertiary structure with secondary structure, which is much easier to obtain and process. Moreover, we applied Rsite2 to three ncRNAs (tRNA (Lys), Diels-Alder ribozyme, and RNase P) and a list of human mitochondria transcripts. The results show that Rsite2 works well with nearly equivalent accuracy as Rsite but is much more feasible and efficient. Finally, a web-server, the source codes, and the dataset of Rsite2 are available at http://www.cuialb.cn/rsite2.
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